Identify objects in images
Identify car damage in images
Find and label objects in images
Identify objects and poses in images
Detect objects in your images
Upload an image to detect objects
Detect objects in images and return details
Identify objects in images
Detect objects in images
Detect objects in images
Identify and label objects in images using YOLO models
Count objects in an image by drawing a region of interest
Detect marine vessels in images
Image Classification Test is an AI-powered tool designed to identify and classify objects within images. It falls under the category of Object Detection and is used to evaluate the performance of image classification models. The tool allows users to analyze images, detect objects, and categorize them into predefined classes with high accuracy. It is a robust solution for both real-time and batch processing of images, making it suitable for various applications.
• Support for Custom Models: Integrate custom-trained models for tailored image classification.
• Multi-Class Classification: Classify images into multiple categories with high precision.
• API Integration: Easily integrate with existing systems via RESTful APIs.
• Performance Metrics: Detailed metrics to evaluate model accuracy and reliability.
• Batch Processing: Process multiple images simultaneously for efficient analysis.
• User-Friendly Interface: Intuitive dashboard for easy configuration and result visualization.
What is Image Classification Test used for?
Image Classification Test is primarily used to evaluate and improve the accuracy of image classification models. It helps in object detection, scene understanding, and model validation.
Which models are supported for image classification?
The tool supports custom-trained models as well as popular pre-trained models such as ResNet, Inception, and MobileNet. Users can upload their own models for specific use cases.
Can Image Classification Test handle large datasets?
Yes, the tool supports batch processing, allowing users to classify multiple images simultaneously. It is optimized for efficiency and scalability, making it suitable for large datasets.